Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations400
Missing cells1,012
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.2 KiB
Average record size in memory208.0 B

Variable types

Numeric6
Categorical14
Boolean5

Alerts

al is highly overall correlated with bu and 6 other fieldsHigh correlation
ane is highly overall correlated with bu and 4 other fieldsHigh correlation
bgr is highly overall correlated with suHigh correlation
bu is highly overall correlated with al and 4 other fieldsHigh correlation
cad is highly overall correlated with rbccHigh correlation
class is highly overall correlated with al and 9 other fieldsHigh correlation
dm is highly overall correlated with class and 5 other fieldsHigh correlation
hemo is highly overall correlated with ane and 3 other fieldsHigh correlation
htn is highly overall correlated with al and 5 other fieldsHigh correlation
pc is highly overall correlated with al and 4 other fieldsHigh correlation
pcc is highly overall correlated with pcHigh correlation
pcv is highly overall correlated with ane and 5 other fieldsHigh correlation
rbc is highly overall correlated with al and 4 other fieldsHigh correlation
rbcc is highly overall correlated with ane and 5 other fieldsHigh correlation
sc is highly overall correlated with al and 8 other fieldsHigh correlation
sg is highly overall correlated with classHigh correlation
sod is highly overall correlated with al and 1 other fieldsHigh correlation
su is highly overall correlated with bgr and 1 other fieldsHigh correlation
su is highly imbalanced (59.6%) Imbalance
pcc is highly imbalanced (51.2%) Imbalance
ba is highly imbalanced (69.0%) Imbalance
cad is highly imbalanced (57.9%) Imbalance
age has 9 (2.2%) missing values Missing
bp has 12 (3.0%) missing values Missing
sg has 47 (11.8%) missing values Missing
al has 46 (11.5%) missing values Missing
su has 49 (12.2%) missing values Missing
rbc has 152 (38.0%) missing values Missing
pc has 65 (16.2%) missing values Missing
bgr has 44 (11.0%) missing values Missing
bu has 19 (4.8%) missing values Missing
sc has 17 (4.2%) missing values Missing
sod has 87 (21.8%) missing values Missing
pot has 88 (22.0%) missing values Missing
hemo has 52 (13.0%) missing values Missing
pcv has 71 (17.8%) missing values Missing
wbcc has 106 (26.5%) missing values Missing
rbcc has 131 (32.8%) missing values Missing

Reproduction

Analysis started2025-07-18 09:19:05.502980
Analysis finished2025-07-18 09:19:26.932899
Duration21.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Missing 

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.483376
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-07-18T17:19:27.070267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.169714
Coefficient of variation (CV)0.33350016
Kurtosis0.057840495
Mean51.483376
Median Absolute Deviation (MAD)10
Skewness-0.66825947
Sum20130
Variance294.79908
MonotonicityNot monotonic
2025-07-18T17:19:27.213079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 19
 
4.8%
65 17
 
4.2%
48 12
 
3.0%
50 12
 
3.0%
55 12
 
3.0%
47 11
 
2.8%
56 10
 
2.5%
59 10
 
2.5%
45 10
 
2.5%
54 10
 
2.5%
Other values (66) 268
67.0%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
0.5%
6 1
 
0.2%
7 1
 
0.2%
8 3
0.8%
11 1
 
0.2%
12 2
0.5%
14 1
 
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
83 1
 
0.2%
82 1
 
0.2%
81 1
 
0.2%
80 4
1.0%
79 1
 
0.2%
78 1
 
0.2%
76 5
1.2%
75 5
1.2%
74 3
0.8%

bp
Categorical

Missing 

Distinct10
Distinct (%)2.6%
Missing12
Missing (%)3.0%
Memory size6.2 KiB
80
116 
70
112 
60
71 
90
53 
100
25 
Other values (5)
 
11

Length

Max length3
Median length2
Mean length2.0798969
Min length2

Characters and Unicode

Total characters807
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row80
2nd row50
3rd row80
4th row70
5th row80

Common Values

ValueCountFrequency (%)
80 116
29.0%
70 112
28.0%
60 71
17.8%
90 53
13.2%
100 25
 
6.2%
50 5
 
1.2%
110 3
 
0.8%
140 1
 
0.2%
180 1
 
0.2%
120 1
 
0.2%
(Missing) 12
 
3.0%

Length

2025-07-18T17:19:27.368783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:27.451432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
80 116
29.9%
70 112
28.9%
60 71
18.3%
90 53
13.7%
100 25
 
6.4%
50 5
 
1.3%
110 3
 
0.8%
140 1
 
0.3%
180 1
 
0.3%
120 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 413
51.2%
8 117
 
14.5%
7 112
 
13.9%
6 71
 
8.8%
9 53
 
6.6%
1 34
 
4.2%
5 5
 
0.6%
4 1
 
0.1%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 413
51.2%
8 117
 
14.5%
7 112
 
13.9%
6 71
 
8.8%
9 53
 
6.6%
1 34
 
4.2%
5 5
 
0.6%
4 1
 
0.1%
2 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 413
51.2%
8 117
 
14.5%
7 112
 
13.9%
6 71
 
8.8%
9 53
 
6.6%
1 34
 
4.2%
5 5
 
0.6%
4 1
 
0.1%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 413
51.2%
8 117
 
14.5%
7 112
 
13.9%
6 71
 
8.8%
9 53
 
6.6%
1 34
 
4.2%
5 5
 
0.6%
4 1
 
0.1%
2 1
 
0.1%

sg
Categorical

High correlation  Missing 

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Memory size6.2 KiB
1.02
106 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.4617564
Min length4

Characters and Unicode

Total characters1,575
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02 106
26.5%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%
(Missing) 47
11.8%

Length

2025-07-18T17:19:27.551687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:27.627454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02 106
30.0%
1.01 84
23.8%
1.025 81
22.9%
1.015 75
21.2%
1.005 7
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

al
Categorical

High correlation  Missing 

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Memory size6.2 KiB
0
199 
1
44 
2
43 
3
43 
4
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters354
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row1
2nd row4
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
(Missing) 46
 
11.5%

Length

2025-07-18T17:19:27.722219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:27.794147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 199
56.2%
1 44
 
12.4%
2 43
 
12.1%
3 43
 
12.1%
4 24
 
6.8%
5 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 199
56.2%
1 44
 
12.4%
2 43
 
12.1%
3 43
 
12.1%
4 24
 
6.8%
5 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 199
56.2%
1 44
 
12.4%
2 43
 
12.1%
3 43
 
12.1%
4 24
 
6.8%
5 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 199
56.2%
1 44
 
12.4%
2 43
 
12.1%
3 43
 
12.1%
4 24
 
6.8%
5 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 199
56.2%
1 44
 
12.4%
2 43
 
12.1%
3 43
 
12.1%
4 24
 
6.8%
5 1
 
0.3%

su
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.2%
Memory size6.2 KiB
0
290 
2
 
18
3
 
14
4
 
13
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters351
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290
72.5%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
1 13
 
3.2%
5 3
 
0.8%
(Missing) 49
 
12.2%

Length

2025-07-18T17:19:27.897309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:28.052649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 290
82.6%
2 18
 
5.1%
3 14
 
4.0%
4 13
 
3.7%
1 13
 
3.7%
5 3
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 290
82.6%
2 18
 
5.1%
3 14
 
4.0%
4 13
 
3.7%
1 13
 
3.7%
5 3
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 290
82.6%
2 18
 
5.1%
3 14
 
4.0%
4 13
 
3.7%
1 13
 
3.7%
5 3
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 290
82.6%
2 18
 
5.1%
3 14
 
4.0%
4 13
 
3.7%
1 13
 
3.7%
5 3
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 290
82.6%
2 18
 
5.1%
3 14
 
4.0%
4 13
 
3.7%
1 13
 
3.7%
5 3
 
0.9%

rbc
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size6.2 KiB
normal
201 
abnormal
47 

Length

Max length8
Median length6
Mean length6.3790323
Min length6

Characters and Unicode

Total characters1,582
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 201
50.2%
abnormal 47
 
11.8%
(Missing) 152
38.0%

Length

2025-07-18T17:19:28.254857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:28.347011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 201
81.0%
abnormal 47
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

pc
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size6.2 KiB
normal
259 
abnormal
76 

Length

Max length8
Median length6
Mean length6.4537313
Min length6

Characters and Unicode

Total characters2,162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowabnormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 259
64.8%
abnormal 76
 
19.0%
(Missing) 65
 
16.2%

Length

2025-07-18T17:19:28.411249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:28.475457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 259
77.3%
abnormal 76
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

pcc
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size6.2 KiB
notpresent
354 
present
42 

Length

Max length10
Median length10
Mean length9.6818182
Min length7

Characters and Unicode

Total characters3,834
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rowpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 354
88.5%
present 42
 
10.5%
(Missing) 4
 
1.0%

Length

2025-07-18T17:19:28.783278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:28.869912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 354
89.4%
present 42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

ba
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size6.2 KiB
notpresent
374 
present
 
22

Length

Max length10
Median length10
Mean length9.8333333
Min length7

Characters and Unicode

Total characters3,894
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rownotpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 374
93.5%
present 22
 
5.5%
(Missing) 4
 
1.0%

Length

2025-07-18T17:19:29.022193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:29.111936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 374
94.4%
present 22
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

bgr
Real number (ℝ)

High correlation  Missing 

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.03652
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-07-18T17:19:29.174347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.281714
Coefficient of variation (CV)0.53555512
Kurtosis4.2255936
Mean148.03652
Median Absolute Deviation (MAD)25
Skewness2.0107732
Sum52701
Variance6285.5902
MonotonicityNot monotonic
2025-07-18T17:19:29.364216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
2.5%
93 9
 
2.2%
100 9
 
2.2%
107 8
 
2.0%
131 6
 
1.5%
140 6
 
1.5%
109 6
 
1.5%
92 6
 
1.5%
117 6
 
1.5%
130 6
 
1.5%
Other values (136) 284
71.0%
(Missing) 44
 
11.0%
ValueCountFrequency (%)
22 1
 
0.2%
70 5
1.2%
74 3
0.8%
75 2
 
0.5%
76 4
1.0%
78 3
0.8%
79 3
0.8%
80 2
 
0.5%
81 3
0.8%
82 3
0.8%
ValueCountFrequency (%)
490 2
0.5%
463 1
0.2%
447 1
0.2%
425 1
0.2%
424 2
0.5%
423 1
0.2%
415 1
0.2%
410 1
0.2%
380 1
0.2%
360 2
0.5%

bu
Real number (ℝ)

High correlation  Missing 

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.425722
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-07-18T17:19:29.506811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.503006
Coefficient of variation (CV)0.87944921
Kurtosis9.3452886
Mean57.425722
Median Absolute Deviation (MAD)16
Skewness2.6343745
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2025-07-18T17:19:29.594556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 15
 
3.8%
25 13
 
3.2%
19 11
 
2.8%
40 10
 
2.5%
15 9
 
2.2%
48 9
 
2.2%
50 9
 
2.2%
18 9
 
2.2%
32 8
 
2.0%
49 8
 
2.0%
Other values (108) 280
70.0%
(Missing) 19
 
4.8%
ValueCountFrequency (%)
1.5 1
 
0.2%
10 2
 
0.5%
15 9
2.2%
16 7
1.8%
17 7
1.8%
18 9
2.2%
19 11
2.8%
20 7
1.8%
21 1
 
0.2%
22 6
1.5%
ValueCountFrequency (%)
391 1
0.2%
322 1
0.2%
309 1
0.2%
241 1
0.2%
235 1
0.2%
223 1
0.2%
219 1
0.2%
217 1
0.2%
215 1
0.2%
208 1
0.2%

sc
Real number (ℝ)

High correlation  Missing 

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.0724543
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-07-18T17:19:29.689248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.7411261
Coefficient of variation (CV)1.8685798
Kurtosis79.304345
Mean3.0724543
Median Absolute Deviation (MAD)0.6
Skewness7.5095383
Sum1176.75
Variance32.960529
MonotonicityNot monotonic
2025-07-18T17:19:29.776921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 40
 
10.0%
1.1 24
 
6.0%
0.5 23
 
5.8%
1 23
 
5.8%
0.9 22
 
5.5%
0.7 22
 
5.5%
0.6 18
 
4.5%
0.8 17
 
4.2%
2.2 10
 
2.5%
1.5 9
 
2.2%
Other values (74) 175
43.8%
(Missing) 17
 
4.2%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 23
5.8%
0.6 18
4.5%
0.7 22
5.5%
0.8 17
4.2%
0.9 22
5.5%
1 23
5.8%
1.1 24
6.0%
1.2 40
10.0%
1.3 8
 
2.0%
ValueCountFrequency (%)
76 1
0.2%
48.1 1
0.2%
32 1
0.2%
24 1
0.2%
18.1 1
0.2%
18 1
0.2%
16.9 1
0.2%
16.4 1
0.2%
15.2 1
0.2%
15 1
0.2%

sod
Categorical

High correlation  Missing 

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Memory size6.2 KiB
135
40 
140
25 
141
22 
139
21 
142
20 
Other values (29)
185 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters939
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)2.2%

Sample

1st row111
2nd row142
3rd row104
4th row114
5th row131

Common Values

ValueCountFrequency (%)
135 40
10.0%
140 25
 
6.2%
141 22
 
5.5%
139 21
 
5.2%
142 20
 
5.0%
138 20
 
5.0%
137 19
 
4.8%
136 17
 
4.2%
150 17
 
4.2%
147 13
 
3.2%
Other values (24) 99
24.8%
(Missing) 87
21.8%

Length

2025-07-18T17:19:29.865429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
135 40
12.8%
140 25
 
8.0%
141 22
 
7.0%
139 21
 
6.7%
142 20
 
6.4%
138 20
 
6.4%
137 19
 
6.1%
150 17
 
5.4%
136 17
 
5.4%
147 13
 
4.2%
Other values (24) 99
31.6%

Most occurring characters

ValueCountFrequency (%)
1 350
37.3%
3 172
18.3%
4 136
 
14.5%
5 72
 
7.7%
0 52
 
5.5%
2 48
 
5.1%
7 35
 
3.7%
6 29
 
3.1%
9 22
 
2.3%
8 22
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 350
37.3%
3 172
18.3%
4 136
 
14.5%
5 72
 
7.7%
0 52
 
5.5%
2 48
 
5.1%
7 35
 
3.7%
6 29
 
3.1%
9 22
 
2.3%
8 22
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 350
37.3%
3 172
18.3%
4 136
 
14.5%
5 72
 
7.7%
0 52
 
5.5%
2 48
 
5.1%
7 35
 
3.7%
6 29
 
3.1%
9 22
 
2.3%
8 22
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 350
37.3%
3 172
18.3%
4 136
 
14.5%
5 72
 
7.7%
0 52
 
5.5%
2 48
 
5.1%
7 35
 
3.7%
6 29
 
3.1%
9 22
 
2.3%
8 22
 
2.3%

pot
Categorical

Missing 

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Memory size6.2 KiB
3.5
30 
5
30 
4.9
27 
4.7
 
17
4.8
 
16
Other values (35)
192 

Length

Max length3
Median length3
Mean length2.6987179
Min length1

Characters and Unicode

Total characters842
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)2.6%

Sample

1st row2.5
2nd row3.2
3rd row4
4th row3.7
5th row4.2

Common Values

ValueCountFrequency (%)
3.5 30
 
7.5%
5 30
 
7.5%
4.9 27
 
6.8%
4.7 17
 
4.2%
4.8 16
 
4.0%
4.2 14
 
3.5%
3.8 14
 
3.5%
3.9 14
 
3.5%
4.4 14
 
3.5%
4.1 14
 
3.5%
Other values (30) 122
30.5%
(Missing) 88
22.0%

Length

2025-07-18T17:19:29.959143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.5 30
 
9.6%
5 30
 
9.6%
4.9 27
 
8.7%
4.7 17
 
5.4%
4.8 16
 
5.1%
4.2 14
 
4.5%
3.8 14
 
4.5%
3.9 14
 
4.5%
4.4 14
 
4.5%
4.1 14
 
4.5%
Other values (30) 122
39.1%

Most occurring characters

ValueCountFrequency (%)
. 264
31.4%
4 172
20.4%
3 114
13.5%
5 106
12.6%
9 47
 
5.6%
7 36
 
4.3%
8 33
 
3.9%
2 29
 
3.4%
6 26
 
3.1%
1 15
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 264
31.4%
4 172
20.4%
3 114
13.5%
5 106
12.6%
9 47
 
5.6%
7 36
 
4.3%
8 33
 
3.9%
2 29
 
3.4%
6 26
 
3.1%
1 15
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 264
31.4%
4 172
20.4%
3 114
13.5%
5 106
12.6%
9 47
 
5.6%
7 36
 
4.3%
8 33
 
3.9%
2 29
 
3.4%
6 26
 
3.1%
1 15
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 264
31.4%
4 172
20.4%
3 114
13.5%
5 106
12.6%
9 47
 
5.6%
7 36
 
4.3%
8 33
 
3.9%
2 29
 
3.4%
6 26
 
3.1%
1 15
 
1.8%

hemo
Real number (ℝ)

High correlation  Missing 

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.526437
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-07-18T17:19:30.048307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.9125866
Coefficient of variation (CV)0.23251517
Kurtosis-0.47139804
Mean12.526437
Median Absolute Deviation (MAD)2.35
Skewness-0.33509468
Sum4359.2
Variance8.4831608
MonotonicityNot monotonic
2025-07-18T17:19:30.140534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 16
 
4.0%
10.9 8
 
2.0%
13.6 7
 
1.8%
13 7
 
1.8%
9.8 7
 
1.8%
11.1 7
 
1.8%
10.3 6
 
1.5%
11.3 6
 
1.5%
13.9 6
 
1.5%
12 6
 
1.5%
Other values (105) 272
68.0%
(Missing) 52
 
13.0%
ValueCountFrequency (%)
3.1 1
0.2%
4.8 1
0.2%
5.5 1
0.2%
5.6 1
0.2%
5.8 1
0.2%
6 2
0.5%
6.1 1
0.2%
6.2 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
17.8 3
0.8%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 1
 
0.2%
17.4 2
0.5%
17.3 1
 
0.2%
17.2 2
0.5%
17.1 2
0.5%
17 4
1.0%
16.9 2
0.5%

pcv
Categorical

High correlation  Missing 

Distinct42
Distinct (%)12.8%
Missing71
Missing (%)17.8%
Memory size6.2 KiB
52
 
21
41
 
21
44
 
19
48
 
19
40
 
16
Other values (37)
233 

Length

Max length2
Median length2
Mean length1.9969605
Min length1

Characters and Unicode

Total characters657
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)2.4%

Sample

1st row44
2nd row38
3rd row31
4th row32
5th row35

Common Values

ValueCountFrequency (%)
52 21
 
5.2%
41 21
 
5.2%
44 19
 
4.8%
48 19
 
4.8%
40 16
 
4.0%
43 15
 
3.8%
45 13
 
3.2%
42 13
 
3.2%
50 12
 
3.0%
32 12
 
3.0%
Other values (32) 168
42.0%
(Missing) 71
17.8%

Length

2025-07-18T17:19:30.227345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
52 21
 
6.4%
41 21
 
6.4%
44 19
 
5.8%
48 19
 
5.8%
40 16
 
4.9%
43 15
 
4.6%
45 13
 
4.0%
42 13
 
4.0%
28 12
 
3.6%
36 12
 
3.6%
Other values (32) 168
51.1%

Most occurring characters

ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

wbcc
Real number (ℝ)

Missing 

Distinct89
Distinct (%)30.3%
Missing106
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean8406.1224
Minimum2200
Maximum26400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2025-07-18T17:19:30.323713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile4500
Q16500
median8000
Q39800
95-th percentile12940
Maximum26400
Range24200
Interquartile range (IQR)3300

Descriptive statistics

Standard deviation2944.4742
Coefficient of variation (CV)0.35027734
Kurtosis6.1506398
Mean8406.1224
Median Absolute Deviation (MAD)1700
Skewness1.6215894
Sum2471400
Variance8669928.3
MonotonicityNot monotonic
2025-07-18T17:19:30.418871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9800 11
 
2.8%
6700 10
 
2.5%
9600 9
 
2.2%
7200 9
 
2.2%
9200 9
 
2.2%
6900 8
 
2.0%
5800 8
 
2.0%
11000 8
 
2.0%
7800 7
 
1.8%
7000 7
 
1.8%
Other values (79) 208
52.0%
(Missing) 106
26.5%
ValueCountFrequency (%)
2200 1
 
0.2%
2600 1
 
0.2%
3800 2
 
0.5%
4100 1
 
0.2%
4200 3
0.8%
4300 6
1.5%
4500 3
0.8%
4700 4
1.0%
4900 1
 
0.2%
5000 5
1.2%
ValueCountFrequency (%)
26400 1
0.2%
21600 1
0.2%
19100 1
0.2%
18900 1
0.2%
16700 1
0.2%
16300 1
0.2%
15700 1
0.2%
15200 2
0.5%
14900 1
0.2%
14600 2
0.5%

rbcc
Categorical

High correlation  Missing 

Distinct45
Distinct (%)16.7%
Missing131
Missing (%)32.8%
Memory size6.2 KiB
5.2
 
18
4.5
 
16
4.9
 
14
4.7
 
11
3.9
 
10
Other values (40)
200 

Length

Max length3
Median length3
Mean length2.8215613
Min length1

Characters and Unicode

Total characters759
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.1%

Sample

1st row5.2
2nd row3.9
3rd row4.6
4th row4.4
5th row5

Common Values

ValueCountFrequency (%)
5.2 18
 
4.5%
4.5 16
 
4.0%
4.9 14
 
3.5%
4.7 11
 
2.8%
3.9 10
 
2.5%
5 10
 
2.5%
4.8 10
 
2.5%
4.6 9
 
2.2%
3.4 9
 
2.2%
6.1 8
 
2.0%
Other values (35) 154
38.5%
(Missing) 131
32.8%

Length

2025-07-18T17:19:30.514198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.2 18
 
6.7%
4.5 16
 
5.9%
4.9 14
 
5.2%
4.7 11
 
4.1%
3.9 10
 
3.7%
5 10
 
3.7%
4.8 10
 
3.7%
4.6 9
 
3.3%
3.4 9
 
3.3%
3.7 8
 
3.0%
Other values (35) 154
57.2%

Most occurring characters

ValueCountFrequency (%)
. 245
32.3%
5 115
15.2%
4 115
15.2%
3 75
 
9.9%
6 52
 
6.9%
2 48
 
6.3%
9 34
 
4.5%
8 27
 
3.6%
7 26
 
3.4%
1 22
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 245
32.3%
5 115
15.2%
4 115
15.2%
3 75
 
9.9%
6 52
 
6.9%
2 48
 
6.3%
9 34
 
4.5%
8 27
 
3.6%
7 26
 
3.4%
1 22
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 245
32.3%
5 115
15.2%
4 115
15.2%
3 75
 
9.9%
6 52
 
6.9%
2 48
 
6.3%
9 34
 
4.5%
8 27
 
3.6%
7 26
 
3.4%
1 22
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 245
32.3%
5 115
15.2%
4 115
15.2%
3 75
 
9.9%
6 52
 
6.9%
2 48
 
6.3%
9 34
 
4.5%
8 27
 
3.6%
7 26
 
3.4%
1 22
 
2.9%

htn
Boolean

High correlation 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size3.9 KiB
False
251 
True
147 
(Missing)
 
2
ValueCountFrequency (%)
False 251
62.7%
True 147
36.8%
(Missing) 2
 
0.5%
2025-07-18T17:19:30.596026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

dm
Boolean

High correlation 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size3.9 KiB
False
261 
True
137 
(Missing)
 
2
ValueCountFrequency (%)
False 261
65.2%
True 137
34.2%
(Missing) 2
 
0.5%
2025-07-18T17:19:30.669020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

cad
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size3.9 KiB
False
364 
True
 
34
(Missing)
 
2
ValueCountFrequency (%)
False 364
91.0%
True 34
 
8.5%
(Missing) 2
 
0.5%
2025-07-18T17:19:30.727223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

appet
Categorical

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size6.2 KiB
good
317 
poor
82 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1,596
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowpoor
4th rowpoor
5th rowgood

Common Values

ValueCountFrequency (%)
good 317
79.2%
poor 82
 
20.5%
(Missing) 1
 
0.2%

Length

2025-07-18T17:19:30.797998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:30.864238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
good 317
79.4%
poor 82
 
20.6%

Most occurring characters

ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

pe
Boolean

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
False
323 
True
76 
(Missing)
 
1
ValueCountFrequency (%)
False 323
80.8%
True 76
 
19.0%
(Missing) 1
 
0.2%
2025-07-18T17:19:30.940626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

ane
Boolean

High correlation 

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
False
339 
True
60 
(Missing)
 
1
ValueCountFrequency (%)
False 339
84.8%
True 60
 
15.0%
(Missing) 1
 
0.2%
2025-07-18T17:19:31.012620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

class
Categorical

High correlation 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
ckd
250 
notckd
150 

Length

Max length6
Median length3
Mean length4.125
Min length3

Characters and Unicode

Total characters1,650
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowckd
2nd rowckd
3rd rowckd
4th rowckd
5th rowckd

Common Values

ValueCountFrequency (%)
ckd 250
62.5%
notckd 150
37.5%

Length

2025-07-18T17:19:31.091378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-18T17:19:31.178705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ckd 250
62.5%
notckd 150
37.5%

Most occurring characters

ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Interactions

2025-07-18T17:19:22.922074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:07.464005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:10.072682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:13.872404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:17.004724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:19.907459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:23.443455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:07.799954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:10.487232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:14.319427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:17.372507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:20.301111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:24.148423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:08.261064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:11.394668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:14.855963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:17.967883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:20.853859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:24.635287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:08.671462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:11.981905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:15.361207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:18.480715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:21.518434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:25.000489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:09.005336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:12.700496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:16.035761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:18.883577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:21.907652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:25.522527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:09.707198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:13.249516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:16.612923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:19.511929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-18T17:19:22.435757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-07-18T17:19:31.242586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agealaneappetbabgrbpbucadclassdmhemohtnpcpccpcvpepotrbcrbccscsgsodsuwbcc
age1.0000.1090.2090.1730.0000.2990.0100.3090.0530.3130.285-0.2300.2730.0000.0000.0540.1920.0430.0000.1410.3500.0000.0250.0000.162
al0.1091.0000.3440.3770.4100.3340.1850.5520.3320.7260.4580.4470.5490.5880.4530.3840.4740.2850.5330.3930.5990.2870.5290.1600.263
ane0.2090.3441.0000.2410.0000.3600.2840.5520.0000.3140.1670.6140.3360.3150.1550.6220.1910.3890.1630.6020.5720.2490.3780.1450.286
appet0.1730.3770.2411.0000.1250.4190.2500.3970.1350.3830.3130.4350.3330.3030.1710.4570.4060.3080.2620.4730.4680.2740.3130.2570.244
ba0.0000.4100.0000.1251.0000.2480.1090.4510.1330.1670.0430.2100.0560.3110.2520.2260.1080.0550.1480.2600.4330.2040.3280.1860.382
bgr0.2990.3340.3600.4190.2481.0000.4060.1950.4970.1920.485-0.3490.3800.3700.3300.2580.3690.4420.4700.2490.3590.1820.2400.6160.110
bp0.0100.1850.2840.2500.1090.4061.0000.0000.0000.4590.2750.3190.3600.2850.1510.3770.1730.1760.3940.3800.2040.1700.2560.1540.196
bu0.3090.5520.5520.3970.4510.1950.0001.0000.4860.2820.381-0.5920.4930.4880.4710.3510.3460.4570.5320.3350.7030.2380.3980.4200.094
cad0.0530.3320.0000.1350.1330.4970.0000.4861.0000.2200.2560.2910.3120.1940.1650.3930.1520.3510.1610.5580.4570.1580.3400.3820.000
class0.3130.7260.3140.3830.1670.1920.4590.2820.2201.0000.5500.6900.5820.4520.2500.7740.3650.4720.5420.7260.6510.7890.5350.3660.000
dm0.2850.4580.1670.3130.0430.4850.2750.3810.2560.5501.0000.4270.6000.2890.1450.5210.2960.4180.3210.5370.5290.4500.4310.5490.093
hemo-0.2300.4470.6140.4350.210-0.3490.319-0.5920.2910.6900.4271.0000.4910.4790.3130.4510.3230.2860.4710.287-0.7260.2610.2120.113-0.165
htn0.2730.5490.3360.3330.0560.3800.3600.4930.3120.5820.6000.4911.0000.3720.1770.6050.3600.3510.2890.6450.6240.4190.4750.3700.020
pc0.0000.5880.3150.3030.3110.3700.2850.4880.1940.4520.2890.4790.3721.0000.5010.5770.4030.3590.4100.5390.5420.3850.4680.2220.126
pcc0.0000.4530.1550.1710.2520.3300.1510.4710.1650.2500.1450.3130.1770.5011.0000.3510.0770.3670.0690.3160.3920.2840.3770.1970.307
pcv0.0540.3840.6220.4570.2260.2580.3770.3510.3930.7740.5210.4510.6050.5770.3511.0000.4580.2960.5320.3160.4050.2980.2710.2460.093
pe0.1920.4740.1910.4060.1080.3690.1730.3460.1520.3650.2960.3230.3600.4030.0770.4581.0000.3000.2820.4560.4400.3520.3240.1650.230
pot0.0430.2850.3890.3080.0550.4420.1760.4570.3510.4720.4180.2860.3510.3590.3670.2960.3001.0000.4160.2450.4390.3270.2590.3290.163
rbc0.0000.5330.1630.2620.1480.4700.3940.5320.1610.5420.3210.4710.2890.4100.0690.5320.2820.4161.0000.4010.5720.4350.4800.2130.278
rbcc0.1410.3930.6020.4730.2600.2490.3800.3350.5580.7260.5370.2870.6450.5390.3160.3160.4560.2450.4011.0000.3890.3170.2830.2180.058
sc0.3500.5990.5720.4680.4330.3590.2040.7030.4570.6510.529-0.7260.6240.5420.3920.4050.4400.4390.5720.3891.0000.4120.4800.3680.127
sg0.0000.2870.2490.2740.2040.1820.1700.2380.1580.7890.4500.2610.4190.3850.2840.2980.3520.3270.4350.3170.4121.0000.4020.1830.152
sod0.0250.5290.3780.3130.3280.2400.2560.3980.3400.5350.4310.2120.4750.4680.3770.2710.3240.2590.4800.2830.4800.4021.0000.2550.293
su0.0000.1600.1450.2570.1860.6160.1540.4200.3820.3660.5490.1130.3700.2220.1970.2460.1650.3290.2130.2180.3680.1830.2551.0000.394
wbcc0.1620.2630.2860.2440.3820.1100.1960.0940.0000.0000.093-0.1650.0200.1260.3070.0930.2300.1630.2780.0580.1270.1520.2930.3941.000

Missing values

2025-07-18T17:19:26.279135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-18T17:19:26.515191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-18T17:19:26.750270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwbccrbcchtndmcadappetpeaneclass
048801.0210NaNnormalnotpresentnotpresent121361.2NaNNaN15.44478005.2yesyesnogoodnonockd
17501.0240NaNnormalnotpresentnotpresentNaN180.8NaNNaN11.3386000NaNnononogoodnonockd
262801.0123normalnormalnotpresentnotpresent423531.8NaNNaN9.6317500NaNnoyesnopoornoyesckd
348701.00540normalabnormalpresentnotpresent117563.81112.511.23267003.9yesnonopooryesyesckd
451801.0120normalnormalnotpresentnotpresent106261.4NaNNaN11.63573004.6nononogoodnonockd
560901.01530NaNNaNnotpresentnotpresent74251.11423.212.23978004.4yesyesnogoodyesnockd
668701.0100NaNnormalnotpresentnotpresent1005424104412.436NaNNaNnononogoodnonockd
724NaN1.01524normalabnormalnotpresentnotpresent410311.1NaNNaN12.44469005noyesnogoodyesnockd
8521001.01530normalabnormalpresentnotpresent138601.9NaNNaN10.83396004yesyesnogoodnoyesckd
953901.0220abnormalabnormalpresentnotpresent701077.21143.79.529121003.7yesyesnopoornoyesckd
agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwbccrbcchtndmcadappetpeaneclass
39052801.02500normalnormalnotpresentnotpresent99250.81353.7155263005.3nononogoodnononotckd
39136801.02500normalnormalnotpresentnotpresent85161.11424.115.64458006.3nononogoodnononotckd
39257801.0200normalnormalnotpresentnotpresent133481.21474.314.84666005.5nononogoodnononotckd
39343601.02500normalnormalnotpresentnotpresent117450.71414.4135474005.4nononogoodnononotckd
39450801.0200normalnormalnotpresentnotpresent137460.8139514.14595004.6nononogoodnononotckd
39555801.0200normalnormalnotpresentnotpresent140490.51504.915.74767004.9nononogoodnononotckd
39642701.02500normalnormalnotpresentnotpresent75311.21413.516.55478006.2nononogoodnononotckd
39712801.0200normalnormalnotpresentnotpresent100260.61374.415.84966005.4nononogoodnononotckd
39817601.02500normalnormalnotpresentnotpresent1145011354.914.25172005.9nononogoodnononotckd
39958801.02500normalnormalnotpresentnotpresent131181.11413.515.85368006.1nononogoodnononotckd